Question 39
Domain 4: Guidelines for Responsible AIA company wants to build a lead prioritization application for its employees to contact potential customers. The application must give employees the ability to view and adjust the weights assigned to different variables in the model based on domain knowledge and expertise. Which ML model type meets these requirements?
Correct answer: A
Explanation
Logistic regression is a classical ML model that produces interpretable weights for each input variable, so employees can “view and adjust the weights assigned to different variables.” That matches the need for domain experts to tune feature importance, unlike opaque models such as neural networks.
Why each option is right or wrong
A. Logistic regression model
Logistic regression is a classical machine learning algorithm whose learned coefficients are explicit, per-feature weights, so the team can inspect and tune how each variable contributes to the score. In practice, the model’s parameters are directly interpretable and can be adjusted by domain experts, unlike neural-network-based models whose internal weights are not meaningfully transparent for business users.
B. Deep learning model built on principal components
Deep learning uses layered neural networks, not transparent variable weights for manual adjustment.
C. K-nearest neighbors (k-NN) model
k-NN stores examples and compares distances; it does not learn adjustable feature weights.
D. Neural network
Neural networks learn internal parameters across layers, but they are not designed for simple weight inspection.